The proliferation of enterprise data is unceasing. Companies are seeing their volumes of enterprise data growing faster than ever before, and that data is coming from more sources than ever. As data volumes grow, analytics become more complex, users demand faster response times, and cost reduction initiatives become rampant. Traditional data warehouse users have simply been unable to keep up with these bottlenecks.
As part of this transformation, increased emphasis is placed on the consolidation, migration, and optimization of data warehouse (DW) database infrastructure. Data warehouse (DW) appliance vendors are deploying massively parallel architectures that take a different approach to data storage than traditional database architectures to eliminate the bottlenecks described above.
As applications that use complex queries and massive amounts of data storage have become increasingly prevalent, a shift from traditional RDBMS (Relational Database Management Systems) to data warehouse appliances is occurring. In particular, as Business Intelligence (BI) applications become more pervasive, use of data warehouse appliances (DWAs or DW appliances) is increasing in order to provide integrated, enterprise-wide data warehouses that assure scalability, query performance, and improved development and maintenance costs. Such DWAs integrate database, server, and storage in a single, easy-to-manage system. These DWAs also typically offer operating systems, DBMS (database management systems), and software tailored for a data warehouse environment using a massively parallel processing architecture to provide high performance and scalability.
Thus, as Business Intelligence emerges as a factor for strategic, tactical, and operational information users, access to information alone is no longer enough. Organizations are using BI to monitor, report, analyze, and improve the performance of business operations. Current business demands require processing large amounts of data to generate relevant analytical reports. As data volumes increase and query navigation becomes more sophisticated, it becomes challenging to provide adequate query performance for large volumes of data that meets response time service level agreements.